




'''simple docstring'''



import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_    	=				logging.get_logger(__name__)




def   lowercase__(			__UpperCamelCase:					Any    ):

      """simple docstring"""






      print('Loading config file...'    )

      def flatten_yaml_as_dict(__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Optional[Any]=""      ,__UpperCamelCase:					List[str]="."    ):
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]
            for k, v in d.items():
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						parent_key + sep + k if parent_key else k
                  if isinstance(__UpperCamelCase      ,collections.abc.MutableMapping    ):
                        items.extend(flatten_yaml_as_dict(__UpperCamelCase      ,__UpperCamelCase      ,sep=__UpperCamelCase    ).items()    )
                  else:
                        items.append((new_key, v)    )
            return dict(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Tuple    					=						argparse.Namespace()
      with open(__UpperCamelCase      ,'r'    ) as yaml_file:
            try:
                  SCREAMING_SNAKE_CASE   :      Dict    					=						yaml.load(__UpperCamelCase      ,Loader=yaml.FullLoader    )

                  SCREAMING_SNAKE_CASE   :      Any    					=						flatten_yaml_as_dict(__UpperCamelCase    )
                  for k, v in flat_cfg.items():
                        setattr(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
            except yaml.YAMLError as exc:
                  logger.error('Error while loading config file: {}. Error message: {}'.format(__UpperCamelCase      ,str(__UpperCamelCase    )    )    )
      return config




def   lowercase__(			__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Any    					=						MobileViTVaConfig()

      SCREAMING_SNAKE_CASE   :      Any    					=						False

      # dataset
      if task_name.startswith('imagenet1k_'    ):
            SCREAMING_SNAKE_CASE   :      Dict    					=						10_00
            if int(task_name.strip().split('_'    )[-1]    ) == 3_84:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						3_84
            else:
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						2_56
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'imagenet-1k-id2label.json'
      elif task_name.startswith('imagenet21k_to_1k_'    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						2_10_00
            if int(task_name.strip().split('_'    )[-1]    ) == 3_84:
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						3_84
            else:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						2_56
            SCREAMING_SNAKE_CASE   :      Dict    					=						'imagenet-22k-id2label.json'
      elif task_name.startswith('ade20k_'    ):
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						1_51
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						5_12
            SCREAMING_SNAKE_CASE   :      Dict    					=						'ade20k-id2label.json'
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						True
      elif task_name.startswith('voc_'    ):
            SCREAMING_SNAKE_CASE   :      Tuple    					=						21
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						5_12
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'pascal-voc-id2label.json'
            SCREAMING_SNAKE_CASE   :      Dict    					=						True

      # orig_config
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						load_orig_config_file(__UpperCamelCase    )
      assert getattr(__UpperCamelCase      ,'model.classification.name'      ,-1    ) == "mobilevit_v2", "Invalid model"
      SCREAMING_SNAKE_CASE   :      Tuple    					=						getattr(__UpperCamelCase      ,'model.classification.mitv2.width_multiplier'      ,1.0    )
      assert (
          getattr(__UpperCamelCase      ,'model.classification.mitv2.attn_norm_layer'      ,-1    ) == "layer_norm_2d"
      ), "Norm layers other than layer_norm_2d is not supported"
      SCREAMING_SNAKE_CASE   :      int    					=						getattr(__UpperCamelCase      ,'model.classification.activation.name'      ,'swish'    )
      # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

      if is_segmentation_model:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						getattr(__UpperCamelCase      ,'model.segmentation.output_stride'      ,16    )
            if "_deeplabv3" in task_name:
                  SCREAMING_SNAKE_CASE   :      str    					=						getattr(__UpperCamelCase      ,'model.segmentation.deeplabv3.aspp_rates'      ,[12, 24, 36]    )
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						getattr(__UpperCamelCase      ,'model.segmentation.deeplabv3.aspp_out_channels'      ,5_12    )
                  SCREAMING_SNAKE_CASE   :      int    					=						getattr(__UpperCamelCase      ,'model.segmentation.deeplabv3.aspp_dropout'      ,0.1    )

    # id2label
      SCREAMING_SNAKE_CASE   :      List[str]    					=						'huggingface/label-files'
      SCREAMING_SNAKE_CASE   :      Dict    					=						json.load(open(hf_hub_download(__UpperCamelCase      ,__UpperCamelCase      ,repo_type='dataset'    )      ,'r'    )    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						{int(__UpperCamelCase    ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						idalabel
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{v: k for k, v in idalabel.items()}

      return config




def   lowercase__(			__UpperCamelCase:					int      ,__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						dct.pop(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Any    					=						val




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					List[str]=False    ):

      """simple docstring"""






      if base_model:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						''
      else:
            SCREAMING_SNAKE_CASE   :      List[str]    					=						'mobilevitv2.'

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]
      for k in state_dict.keys():
            if k[:8] == "encoder.":
                  SCREAMING_SNAKE_CASE   :      int    					=						k[8:]
            else:
                  SCREAMING_SNAKE_CASE   :      Optional[int]    					=						k

            if ".block." in k:
                  SCREAMING_SNAKE_CASE   :      str    					=						k_new.replace('.block.'      ,'.'    )
            if ".conv." in k:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						k_new.replace('.conv.'      ,'.convolution.'    )
            if ".norm." in k:
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						k_new.replace('.norm.'      ,'.normalization.'    )

            if "conv_1." in k:
                  SCREAMING_SNAKE_CASE   :      Any    					=						k_new.replace('conv_1.'      ,f"{model_prefix}conv_stem."    )
            for i in [1, 2]:
                  if f"layer_{i}." in k:
                        SCREAMING_SNAKE_CASE   :      List[str]    					=						k_new.replace(f"layer_{i}."      ,f"{model_prefix}encoder.layer.{i-1}.layer."    )
            if ".exp_1x1." in k:
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						k_new.replace('.exp_1x1.'      ,'.expand_1x1.'    )
            if ".red_1x1." in k:
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						k_new.replace('.red_1x1.'      ,'.reduce_1x1.'    )

            for i in [3, 4, 5]:
                  if f"layer_{i}.0." in k:
                        SCREAMING_SNAKE_CASE   :      List[str]    					=						k_new.replace(f"layer_{i}.0."      ,f"{model_prefix}encoder.layer.{i-1}.downsampling_layer."    )
                  if f"layer_{i}.1.local_rep.0." in k:
                        SCREAMING_SNAKE_CASE   :      List[str]    					=						k_new.replace(f"layer_{i}.1.local_rep.0."      ,f"{model_prefix}encoder.layer.{i-1}.conv_kxk."    )
                  if f"layer_{i}.1.local_rep.1." in k:
                        SCREAMING_SNAKE_CASE   :      Dict    					=						k_new.replace(f"layer_{i}.1.local_rep.1."      ,f"{model_prefix}encoder.layer.{i-1}.conv_1x1."    )

            for i in [3, 4, 5]:
                  if i == 3:
                        SCREAMING_SNAKE_CASE   :      int    					=						[0, 1]
                  elif i == 4:
                        SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[0, 1, 2, 3]
                  elif i == 5:
                        SCREAMING_SNAKE_CASE   :      List[str]    					=						[0, 1, 2]

                  for j in j_in:
                        if f"layer_{i}.1.global_rep.{j}." in k:
                              SCREAMING_SNAKE_CASE   :      Tuple    					=						k_new.replace(
                                  f"layer_{i}.1.global_rep.{j}."      ,f"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."    )
                  if f"layer_{i}.1.global_rep.{j+1}." in k:
                        SCREAMING_SNAKE_CASE   :      List[Any]    					=						k_new.replace(
                            f"layer_{i}.1.global_rep.{j+1}."      ,f"{model_prefix}encoder.layer.{i-1}.layernorm."    )

                  if f"layer_{i}.1.conv_proj." in k:
                        SCREAMING_SNAKE_CASE   :      List[str]    					=						k_new.replace(f"layer_{i}.1.conv_proj."      ,f"{model_prefix}encoder.layer.{i-1}.conv_projection."    )

            if "pre_norm_attn.0." in k:
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						k_new.replace('pre_norm_attn.0.'      ,'layernorm_before.'    )
            if "pre_norm_attn.1." in k:
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						k_new.replace('pre_norm_attn.1.'      ,'attention.'    )
            if "pre_norm_ffn.0." in k:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						k_new.replace('pre_norm_ffn.0.'      ,'layernorm_after.'    )
            if "pre_norm_ffn.1." in k:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						k_new.replace('pre_norm_ffn.1.'      ,'ffn.conv1.'    )
            if "pre_norm_ffn.3." in k:
                  SCREAMING_SNAKE_CASE   :      int    					=						k_new.replace('pre_norm_ffn.3.'      ,'ffn.conv2.'    )

            if "classifier.1." in k:
                  SCREAMING_SNAKE_CASE   :      int    					=						k_new.replace('classifier.1.'      ,'classifier.'    )

            if "seg_head." in k:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						k_new.replace('seg_head.'      ,'segmentation_head.'    )
            if ".aspp_layer." in k:
                  SCREAMING_SNAKE_CASE   :      Any    					=						k_new.replace('.aspp_layer.'      ,'.'    )
            if ".aspp_pool." in k:
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						k_new.replace('.aspp_pool.'      ,'.'    )

            rename_keys.append((k, k_new)    )
      return rename_keys




def   lowercase__(			__UpperCamelCase:					Tuple    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[str]    					=						[]
      for k in state_dict.keys():
            if k.startswith('seg_head.aux_head.'    ):
                  keys_to_ignore.append(__UpperCamelCase    )
      for k in keys_to_ignore:
            state_dict.pop(__UpperCamelCase      ,__UpperCamelCase    )




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[str]    					=						'http://images.cocodataset.org/val2017/000000039769.jpg'
      # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						Image.open(requests.get(__UpperCamelCase      ,stream=__UpperCamelCase    ).raw    )
      return im




@torch.no_grad()
def   lowercase__(			__UpperCamelCase:					int      ,__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						get_mobilevitva_config(__UpperCamelCase      ,__UpperCamelCase    )

      # load original state_dict
      SCREAMING_SNAKE_CASE   :      Dict    					=						torch.load(__UpperCamelCase      ,map_location='cpu'    )

      # load huggingface model
      if task_name.startswith('ade20k_'    ) or task_name.startswith('voc_'    ):
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						MobileViTVaForSemanticSegmentation(__UpperCamelCase    ).eval()
            SCREAMING_SNAKE_CASE   :      List[str]    					=						False
      else:
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						MobileViTVaForImageClassification(__UpperCamelCase    ).eval()
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						False

      # remove and rename some keys of load the original model
      SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint
      remove_unused_keys(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						create_rename_keys(__UpperCamelCase      ,base_model=__UpperCamelCase    )
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )

      # load modified state_dict
      model.load_state_dict(__UpperCamelCase    )

      # Check outputs on an image, prepared by MobileViTImageProcessor
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						MobileViTImageProcessor(crop_size=config.image_size      ,size=config.image_size + 32    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image_processor(images=prepare_img()      ,return_tensors='pt'    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						model(**__UpperCamelCase    )

      # verify classification model
      if task_name.startswith('imagenet'    ):
            SCREAMING_SNAKE_CASE   :      Any    					=						outputs.logits
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						logits.argmax(-1    ).item()
            print('Predicted class:'      ,model.config.idalabel[predicted_class_idx]    )
            if task_name.startswith('imagenet1k_256'    ) and config.width_multiplier == 1.0:
                  # expected_logits for base variant
                  SCREAMING_SNAKE_CASE   :      Any    					=						torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]    )
                  assert torch.allclose(logits[0, :3]      ,__UpperCamelCase      ,atol=1e-4    )

      Path(__UpperCamelCase    ).mkdir(exist_ok=__UpperCamelCase    )
      print(f"Saving model {task_name} to {pytorch_dump_folder_path}"    )
      model.save_pretrained(__UpperCamelCase    )
      print(f"Saving image processor to {pytorch_dump_folder_path}"    )
      image_processor.save_pretrained(__UpperCamelCase    )


if __name__ == "__main__":
      UpperCamelCase_    	=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--task",
          default="imagenet1k_256",
          type=str,
          help=(
              "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
              "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
          ),
          choices=[
              "imagenet1k_256",
              "imagenet1k_384",
              "imagenet21k_to_1k_256",
              "imagenet21k_to_1k_384",
              "ade20k_deeplabv3",
              "voc_deeplabv3",
          ],
      )

      parser.add_argument(
          "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
      )
      parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
      parser.add_argument(
          "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
      )

      UpperCamelCase_    	=				parser.parse_args()
      convert_mobilevitva_checkpoint(
          args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
      )







'''simple docstring'''

from __future__ import annotations






def 				a  (     UpperCamelCase_    :    str      ,					UpperCamelCase_    :    list[str] | None = None      ,					UpperCamelCase_    :    dict[str, float] | None = None      ,					UpperCamelCase_    :    bool = False      ,					)       ->	tuple[int, float, str]:
		snake_case__	     =cipher_alphabet or [chr(UpperCamelCase_			) for i in range(97      ,					123			)]

		# If the argument is None or the user provided an empty dictionary
		if not frequencies_dict:
				# Frequencies of letters in the english language (how much they show up)
				snake_case__	     ={
				    'a': 0.0_8_4_9_7,
				    'b': 0.0_1_4_9_2,
				    'c': 0.0_2_2_0_2,
				    'd': 0.0_4_2_5_3,
				    'e': 0.1_1_1_6_2,
				    'f': 0.0_2_2_2_8,
				    'g': 0.0_2_0_1_5,
				    'h': 0.0_6_0_9_4,
				    'i': 0.0_7_5_4_6,
				    'j': 0.0_0_1_5_3,
				    'k': 0.0_1_2_9_2,
				    'l': 0.0_4_0_2_5,
				    'm': 0.0_2_4_0_6,
				    'n': 0.0_6_7_4_9,
				    'o': 0.0_7_5_0_7,
				    'p': 0.0_1_9_2_9,
				    'q': 0.0_0_0_9_5,
				    'r': 0.0_7_5_8_7,
				    's': 0.0_6_3_2_7,
				    't': 0.0_9_3_5_6,
				    'u': 0.0_2_7_5_8,
				    'v': 0.0_0_9_7_8,
				    'w': 0.0_2_5_6_0,
				    'x': 0.0_0_1_5_0,
				    'y': 0.0_1_9_9_4,
				    'z': 0.0_0_0_7_7,
				}
		else:
				# Custom frequencies dictionary
				snake_case__	     =frequencies_dict

		if not case_sensitive:
				snake_case__	     =ciphertext.lower()

		# Chi squared statistic values
		snake_case__	     ={}

		# cycle through all of the shifts
		for shift in range(len(UpperCamelCase_			)			):
				snake_case__	     =''

				# decrypt the message with the shift
				for letter in ciphertext:
						try:
								# Try to index the letter in the alphabet
								snake_case__	     =(alphabet_letters.index(letter.lower()			) - shift) % len(
								    UpperCamelCase_			)
								decrypted_with_shift += (
								    alphabet_letters[new_key].upper()
								    if case_sensitive and letter.isupper()
								    else alphabet_letters[new_key]
								)
						except ValueError:
								# Append the character if it isn't in the alphabet
								decrypted_with_shift += letter

				snake_case__	     =0.0

				# Loop through each letter in the decoded message with the shift
				for letter in decrypted_with_shift:
						if case_sensitive:
								snake_case__	     =letter.lower()
								if letter in frequencies:
										# Get the amount of times the letter occurs in the message
										snake_case__	     =decrypted_with_shift.lower().count(UpperCamelCase_			)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										snake_case__	     =frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										snake_case__	     =((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value
						else:
								if letter.lower() in frequencies:
										# Get the amount of times the letter occurs in the message
										snake_case__	     =decrypted_with_shift.count(UpperCamelCase_			)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										snake_case__	     =frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										snake_case__	     =((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
				snake_case__	     =(
				    chi_squared_statistic,
				    decrypted_with_shift,
				)

		# Get the most likely cipher by finding the cipher with the smallest chi squared
		# statistic
		def chi_squared_statistic_values_sorting_key(UpperCamelCase_    :    int			) -> tuple[float, str]:
				return chi_squared_statistic_values[key]

		snake_case__	     =min(
		    UpperCamelCase_      ,					key=UpperCamelCase_      ,					)

		# Get all the data from the most likely cipher (key, decoded message)
		(
		    (
		    snake_case__
		)     ,	(
		    snake_case__
		)     ,	
		)	     =chi_squared_statistic_values[most_likely_cipher]

		# Return the data on the most likely shift
		return (
		    most_likely_cipher,
		    most_likely_cipher_chi_squared_value,
		    decoded_most_likely_cipher,
		)




